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Abstract   PyTorch is an open-source machine learning library primarily used for building and training deep learning models. Its key features and fundamental concepts include:   1. Tensors: Tensors are the fundamental data structure in PyTorch, similar to NumPy arrays but with GPU acceleration capabilities. They represent multi-dimensional arrays and are used to store data, model parameters, and intermediate computations. Operations on tensors are optimized for performance, especially on GPUs. 2. Autograd (Automatic Differentiation): PyTorch's  autograd  engine automatically computes gradients for all operations on tensors with  requires_grad=True . This is crucial for backpropagation in neural networks, where gradients are used to update model parameters during training. It builds a dynamic computation graph, allowing for flexible model architectures and conditional computations. 3.  torch.nn  Module: This module provides ...

Abstract : Deep Learning is a subfield of Machine Learning inspired by the structure and function of the human brain, utilizing artificial neural networks to learn from data. These networks consist of interconnected "neurons" organized in layers, including an input layer, one or more hidden layers, and an output layer. Deep learning excels at tasks involving complex pattern recognition in large datasets, such as image classification, natural language processing, and speech recognition. PyTorch is an open-source machine learning framework built on the Torch library and Python. It has become a popular choice for deep learning research and development due to its:   Pythonic Interface:   PyTorch's API is designed to be intuitive and integrate seamlessly with the Python ecosystem, making it accessible for developers familiar with Python. Dynamic Computation Graph:   Unlike some other frameworks, PyTorch uses a dynamic computation graph, allow...